A Saliency Based Human Detection Framework for Infrared Thermal Images

In this paper, a novel saliency framework for crowd detection in infrared thermal images is proposed. In order to obtain the optimal classifier from a large amount of data, the process of training consists of the following four steps: (a) a saliency contrast algorithm is employed to detect the regions of interest; (b) standard HOG features of the selected interest areas are extracted to represent the human object; (c) the extracted features, which are prepared for training, are optimized based on a visual attention map; (d) a support vector machine (SVM) algorithm is applied to compute the classifier. Finally, we can detect the human precisely after high-saliency areas of an image are input into the classifier. In order to evaluate our algorithm, we constructed an infrared thermal image database collected by a real-time inspection system. The experimental results demonstrated that our method can outperform the previous state-of-the art methods for human detection in infrared thermal images, and the visual attentional techniques can effectively represent prior knowledge for features optimization in a practicable system.